P437: SURVIVAL-BASED 4-GENE PROGNOSTIC INDEX IMPROVES THE ELN-RISK CLASSIFICATION IN ACUTE MYELOID LEUKEMIA
Authors: C. A. Ortiz Rojas, D. A. Pereira-Martins, C. C. Bellido More, J. R. Hilberink, J. L. Coelho-Silva, I. Weinhäuser, L. Yamamoto de Almeida, V. M. de Deus Wagatsuma, C. Hassibe Thomé, G. Aguiar Ferreira, E. Ammatuna, G. Huls, J. J. Schuringa, E. Magalhães Rego
Journal: HemaSphere (2022)

Background: The European Leukemia-Net (ELN) criteria have been routinely used for risk categorization in acute myeloid leukemia (AML), classifying patients into favorable, intermediate and adverse-risk groups based on the presence of specific chromosomal and molecular alterations (Döhner et al., 2017). Despite this classification, high heterogeneity in the disease course is still a clinical barrier since leukemia can progress differently than expected. In this sense, the identification of markers that could improve the risk prediction is an unmet medical need. Aims: We decide to perform a multicohort analysis of transcriptomic data to identify a gene signature consistently associated with prognosis in AML patients. Methods: Publicly available data of five AML cohorts treated with intensive chemotherapy were included in this study: TCGA, n=122; BeatAML, n=230; HOVON (GSE6891), n=392; NTUH (GSE71014), n=104; German (GSE37642), n=136. Also, we retrospectively included 38 patients diagnosed with AML, treated with intensive chemotherapy at the University Medical Center Groningen, in order to validate our gene signature by qRT-PCR. Univariate and multivariate Cox regression and area under the ROC curve (AUC) was applied separately to each gene in each cohort, in order to filter genes associated with prognosis. Prognostic indexes (PIs) models were generated by weighted sum using the pooled β coefficient. Then, the best PI was evaluated in its ability of improving ELN-risk classification. Results: We found seven genes whose expression levels were associated with overall survival. After modeling possible PIs, we selected the PI that generates a better model in combination with ELN classification by using the Akaike’s Criterion Information (AIC) estimator. Thus, we selected a 4-gene prognostic index (4-PI) composed of CYP2E1, DHCR7, IL2RA, and SQLE genes. Survival stratification by 4-PI resulted in 5-year survival rates of 44.7 vs. 7.8% in the TCGA cohort, 28.5 vs. 0% in the BeatAML cohort, 47.7 vs. 26.6% in the HOVON cohort, 41.5 vs. 9.1% in the German cohort, and 88.7 vs. 44.1% in the NTUH cohort. Next, after correction for confounders variables, the 4-PI showed to be independently associated with prognosis, with an HR=3.94, 2.1, and 1.78 for TCGA, BeatAML, and HOVON cohorts, respectively. Among the patient molecular features, we found that inv(16) and dmCEBPA cases were more frequent in the low 4-PI, while TP53 alterations were frequent in patients with a higher 4-PI. Next, we evaluated the utility of the 4-PI in recategorizing patients from the ELN-risk subgroups in a best-fitted risk category. In the BeatAML cohort, ELN2017-adverse patients represented 36.9% (n=85) of patients, with a median OS of 12.2 months, but in the integrated risk, the adverse group increased to 38.7% (n=89) with a median OS of 10.3 months. We found similar results after integration of the ELN2010 with our 4-PI in the TCGA and HOVON cohorts. This power of stratification was maintained after applying the revised ELN proposed by Herold et al., 2020. Next, in our validation cohort, when we integrated the ELN2017 with the 4-PI, 14 patients were recategorized from the intermediate to the adverse group, improving the difference between them (median OS=33.8 vs. 7.6 months). Finally, we found a consistent enrichment of cholesterol metabolism related-pathways in cases with high 4- PI in all the cohorts. Summary/Conclusion: We conclude that our 4-PI outperforms currently used prognosis predictors in AML and its incorporation into ELN categorization can improve risk stratification.